The application proposes a career development and research plan for Dr. Arnab Maity, a statistically trained post-doctoral fellow in the Department of Biostatistics at Harvard School of Public Health (HSPH), committed to a research career in the development of statistical methodology for the analysis of high-dimensional gene and environment data. The applicant will be mentored by Dr. Xihong Lin in the statistical analysis of high-dimensional data, and co-mentored by Dr. Joel Schwartz in environmental epidemiology and exposure biology, and Dr. Xiaole Liu in computational biology and the analysis of high-throughput sequencing data. The proposed research concerns two major aims: (1) quantifying and analyzing DNA methylation, one of the most understood epigenetic markers in the human genome and developing statistical methodology to investigate its association to environmental exposure to heavy metal and air particles and various markers of cardiovascular disease, the leading cause of death worldwide, and (2) developing robust and efficient statistical testing procedures for genetic and environmental effects in high-dimensional genome-wide association studies (GWAS) in the presence of gene-gene and gene-environment interactions and incorporating longitudinal measures of phenotypes. The career development plan and accomplishment of the research aims will be facilitated by training directed by the primary mentor and co-mentors, rigorous coursework, participation in ongoing gene-environmental research projects, various scientific meetings and seminars, and the rich research community available within HSPH, which focuses on gene, environment, and health studies. The applicant has readily available data sets on genome-wide DNA methylation study in the Normative Aging Study (NAS) and the genome-wide association studies of Framingham Heart Study. The proposed methods will be applied to these data sets to draw valuable conclusions regarding the interplay of DNA methylation and other genetic variants, and environmental exposures in relation to susceptibility to cardiovascular disease. Software packages implementing the proposed methods will be developed and made freely available. Public Health Relevance: The investigators will develop statistical methodology to analyze high-dimensional gene and environmental data and their interplay in relation to human health. They will identify genetic and environmental exposure factors that are associated with chronic diseases, such as heart disease, stroke, diabetes, and hypertension.